Cover not available
Article published In:
Journal of Second Language Studies
Vol. 6:1 (2023) ► pp.1–28

Assessing receptive vocabulary using state‑of‑the‑art natural language processing techniques

 | Vanderbilt University, United States
 | Vanderbilt University, United States
Semantic embedding approaches commonly used in natural language processing such as transformer models have rarely been used to examine L2 lexical knowledge. Importantly, their performance has not been contrasted with more traditional annotation approaches to lexical knowledge. This study used NLP techniques related to lexical annotations and semantic embedding approaches to model the receptive vocabulary of L2 learners based on their lexical production during a writing task. The goal of the study is to examine the strengths and weaknesses of both approaches in understanding L2 lexical knowledge. Findings indicate that transformer approaches based on semantic embeddings outperform linguistic annotations and Word2vec models in predicting L2 learners’ vocabulary scores. The findings help to support the strength and accuracy of semantic-embedding approaches as well as their generalizability across tasks when compared to linguistic feature models. Limitations to semantic-embedding approaches, especially interpretability, are discussed.
Keywords: natural language processing, corpus linguistics, lexical knowledge, Doc2Vec, BERT, word-embeddings, lexical annotations
Article outline
  • 1.Introduction
  • 2.Literature review
    • 2.1Lexical knowledge
    • 2.2Measuring L2 lexical knowledge
  • 3.Current study
  • 4.Method
    • 4.1Corpus
    • 4.2Receptive vocabulary knowledge
    • 4.3Lexical annotations
      • Age of acquisition
      • Concreteness
      • Word familiarity
      • Word meaningfulness
      • Lexical response times
      • Word associations
      • Phonological distance
      • Word frequency
      • Collocation strength
      • Contextual distinctiveness
    • 4.4Semantic embedding
      • Doc2vec
      • Transformers
    • 4.5Statistical analysis
  • 5.Results
    • 5.1Lexical annotations model
    • 5.2Doc2vec model
    • 5.3BERT model
    • 5.4Comparisons between models
  • 6.Discussion
  • 7.Conclusion
  • Notes
  • References
Published online: 20 September 2022
DOI logo
https://doi.org/10.1075/jsls.22006.cro
References (89)
References
Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 581, 82–115. DOI logoGoogle Scholar
Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. H., & Yap, M. (2004). Visual word recognition of single-syllable words. Journal of Experimental Psychology. General, 133 (2), 283–316. DOI logoGoogle Scholar
Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 391, 445–459. DOI logoGoogle Scholar
Berger, C., Crossley, S., & Kyle, K. (2019). Using native-speaker psycholinguistic norms to predict lexical proficiency and development in second-language production. Applied Linguistics, 40 (1), 22–42. DOI logoGoogle Scholar
Berger, C., Crossley, S., & Skalicky, S. (2019). Using lexical features to investigate second language lexical decision performance. Studies in Second Language Acquisition, 41 (5), 911–935. DOI logoGoogle Scholar
Biber, D. (1988). Variation across Speech and Writing. Cambridge University Press. DOI logoGoogle Scholar
Biber, D., Gray, B., & Staples, S. (2016). Predicting Patterns of Grammatical Complexity Across Language Exam Task Types and Proficiency Levels. Applied Linguistics, 37 (5), 639–668. DOI logoGoogle Scholar
Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc.Google Scholar
BNC Consortium, The British National Corpus, XML Edition, (2007), Oxford Text Archive, [URL]
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. ArXiv:1607.04606 [Cs]. [URL]. DOI logo
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv:2005.14165 [Cs]. [URL]
Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41 (4), 977–990. DOI logoGoogle Scholar
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46 (3), 904–911. DOI logoGoogle Scholar
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019). What Does BERT Look At? An Analysis of BERT’s Attention (arXiv:1906.04341). arXiv. [URL]
Cobb, T. (n.d.). Web Vocabprofile. [URL]
Conrad, S. (2005). Corpus Linguistics and L2 Teaching. In Handbook of Research in Second Language Teaching and Learning. Routledge.Google Scholar
Crossley, S. A., & Kyle, K. (2022). Managing Second Language Acquisition Data with Natural Language Processing Tools. In The Open Handbook of Linguistic Data Management (pp. 411–421). The MIT Press. DOI logoGoogle Scholar
Crossley, S. A., Salsbury, T., McNamara, D. S., & Jarvis, S. (2011a). What Is Lexical Proficiency? Some Answers from Computational Models of Speech Data. TESOL Quarterly: A Journal for Teachers of English to Speakers of Other Languages and of Standard English as a Second Dialect, 45 (1), 182–193. DOI logoGoogle Scholar
(2011b). Predicting lexical proficiency in language learner texts using computational indices. Language Testing, 28 (4), 561–580. DOI logoGoogle Scholar
Crossley, S. A., & Skalicky, S. (2019). Examining Lexical Development in Second Language Learners: An Approximate Replication of Salsbury, Crossley & McNamara (2011). Language Teaching, 52 (3), 385–405. DOI logoGoogle Scholar
Crossley, S. A., Skalicky, S., Kyle, K., & Monteiro, K. (2019). Absolute frequency effects in second language lexical acquisition. Studies in Second Language Acquisition, 41 (4), 721–744. DOI logoGoogle Scholar
Crossley, S., Salsbury, T., & McNamara, D. (2009). Measuring L2 lexical growth using hypernymic relationships. Language Learning, 59 (2), 307–334. DOI logoGoogle Scholar
(2010). The Development of Polysemy and Frequency Use in English Second Language Speakers: Polysemy and Frequency Use in English L2 Speakers. Language Learning, 60 (3), 573–605. DOI logoGoogle Scholar
David, A. (2008). Vocabulary breadth in French L2 learners. The Language Learning Journal, 36 (2), 167–180. DOI logoGoogle Scholar
Davies, M. (2010). The Corpus of Contemporary American English as the first reliable monitor corpus of English. Literary and Linguistic Computing, 25 (4), 447–464. DOI logoGoogle Scholar
Devlin, J., Chang, M. -W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. [URL]
Došilović, F. K., Brčić, M., & Hlupić, N. (2018). Explainable artificial intelligence: A survey. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 0210–0215. DOI logoGoogle Scholar
Ellis, N. C. (2002). Frequency effects in language processing: A Review with Implications for Theories of Implicit and Explicit Language Acquisition. Studies in Second Language Acquisition, 24 (2), 143–188. DOI logoGoogle Scholar
Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press. DOI logoGoogle Scholar
Garner, J., & Crossley, S. (2018). A Latent Curve Model Approach to Studying L2 N-Gram Development. The Modern Language Journal, 102 (3), 494–511. DOI logoGoogle Scholar
Garner, J., Crossley, S., & Kyle, K. (2018). Beginning and intermediate L2 writer’s use of N-grams: An association measures study. International Review of Applied Linguistics in Language Teaching, 58 (1), 51–74. DOI logoGoogle Scholar
Goldberg, Y. (2019). Assessing BERT’s Syntactic Abilities (arXiv:1901.05287). arXiv. DOI logoGoogle Scholar
Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36 (2), 193–202. DOI logoGoogle Scholar
Grant, L., & Ginther, A. (2000). Using Computer-Tagged Linguistic Features to Describe L2 Writing Differences. Journal of Second Language Writing, 9 (2), 123–145. DOI logoGoogle Scholar
Gunning, D., & Aha, D. (2019). DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40 (2), 44–58. DOI logoGoogle Scholar
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. -Z. (2019). XAI-Explainable artificial intelligence. Science Robotics, 4 (37), eaay7120. DOI logoGoogle Scholar
Hashimoto, B. J., & Egbert, J. (2019). More Than Frequency? Exploring Predictors of Word Difficulty for Second Language Learners. Language Learning, 69 (4), 839–872. DOI logoGoogle Scholar
Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A. (2018). Dependency parsing of learner English. International Journal of Corpus Linguistics, 23(1), 28–54. DOI logoGoogle Scholar
Ishikawa, S. (2013). The ICNALE and sophisticated contrastive interlanguage analysis of Asian learners of English. Learner Corpus Studies in Asia and the World, 1 1, 91–118.Google Scholar
Ke, Z., & Ng, V. (2019). Automated Essay Scoring: A Survey of the State of the Art. 6300–6308.Google Scholar
Kerz, E., Wiechmann, D., Qiao, Y., Tseng, E., & Ströbel, M. (2021). Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs. Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, 199–209. [URL]
Kohavi, R., & John, G. H. (1995). Automatic Parameter Selection by Minimizing Estimated Error. In A. Prieditis & S. Russell. (Eds.), Machine Learning Proceedings 1995 (pp. 304–312). Morgan Kaufmann. DOI logoGoogle Scholar
Koizumi, R., & In’nami, Y. (2013). Vocabulary Knowledge and Speaking Proficiency among Second Language Learners from Novice to Intermediate Levels. Journal of Language Teaching and Research, 4 (5), 900–913. DOI logoGoogle Scholar
Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28 1, 1–26. DOI logoGoogle Scholar
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44 (4), 978–990. DOI logoGoogle Scholar
Kyle, K., & Crossley, S. (2016). The relationship between lexical sophistication and independent and source-based writing. Journal of Second Language Writing, 34 1, 12–24. DOI logoGoogle Scholar
Kyle, K., & Crossley, S. A. (2015). Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application. TESOL Quarterly, 49 (4), 757–786. DOI logoGoogle Scholar
Kyle, K., Crossley, S., & Berger, C. (2018). The Tool for the Automatic Analysis of Lexical Sophistication (TAALES): Version 2.0. Behavior Research Methods, 50 (3), 1030–1046. DOI logoGoogle Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104 (2), 211–240. DOI logoGoogle Scholar
Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (Eds.). (2007). Handbook of Latent Semantic Analysis. Psychology Press. DOI logoGoogle Scholar
Lau, J. H., & Baldwin, T. (2016). An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation. Proceedings of the 1st Workshop on Representation Learning for NLP, 78–86. DOI logoGoogle Scholar
Laufer, B., & Nation, P. (1995). Vocabulary Size and Use: Lexical Richness in L2 Written Production. Applied Linguistics, 16 (3), 307–322. DOI logoGoogle Scholar
Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. ArXiv:1405.4053 [Cs]. [URL]
Lemhöfer, K., Dijkstra, T., Schriefers, H., Baayen, R. H., Grainger, J., & Zwitserlood, P. (2008). Native language influences on word recognition in a second language: A megastudy. Journal of Experimental Psychology. Learning, Memory, and Cognition, 34 (1), 12–31. DOI logoGoogle Scholar
Lu, Xiaofei. (2012). The relationship of lexical richness to the quality of ESL learners’ oral narratives. The Modern Language Journal, 96(2), 190–208. DOI logoGoogle Scholar
Lu, X., & Hu, R. (2021). Sense-aware lexical sophistication indices and their relationship to second language writing quality. Behavior Research Methods. DOI logoGoogle Scholar
McDonald, S. A., & Shillcock, R. C. (2001). Rethinking the Word Frequency Effect: The Neglected Role of Distributional Information in Lexical Processing. Language and Speech, 44 (3), 295–322. DOI logoGoogle Scholar
Meara, P. (1996). The dimensions of lexical competence. Performance and Competence in Second Language Acquisition, 35 1, 33–55.Google Scholar
(2005a). Designing vocabulary tests for English. The Dynamics of Language Use: Functional and Contrastive Perspectives, 140 1, 271. DOI logoGoogle Scholar
(2005b). Lexical frequency profiles: A Monte Carlo analysis. Applied Linguistics, 26 (1), 32–47. DOI logoGoogle Scholar
(2010). The relationship between L2 vocabulary knowledge and L2 vocabulary use. The Continuum Companion to Second Language Acquisition, 179–193.Google Scholar
Meurers, D. (2012). Natural Language Processing and Language Learning. In The Encyclopedia of Applied Linguistics. John Wiley & Sons, Ltd. DOI logoGoogle Scholar
(2021). Natural Language Processing and Language Learning. In The Encyclopedia of Applied Linguistics. John Wiley & Sons, Ltd. DOI logoGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ArXiv:1301.3781 [Cs]. DOI logoGoogle Scholar
Milton, J. (2009). Measuring Second Language Vocabulary Acquisition. In Measuring Second Language Vocabulary Acquisition. Multilingual Matters. DOI logoGoogle Scholar
Moghadam, S. H., Zainal, Z., & Ghaderpour, M. (2012). A review on the important role of vocabulary knowledge in reading comprehension performance. Procedia-Social and Behavioral Sciences, 661, 555–563. DOI logoGoogle Scholar
Monteiro, K. R., Crossley, S. A., & Kyle, K. (2020). In Search of New Benchmarks: Using L2 Lexical Frequency and Contextual Diversity Indices to Assess Second Language Writing. Applied Linguistics, 41(2), 280–300. DOI logoGoogle Scholar
Morris, L., & Cobb, T. (2004). Vocabulary profiles as predictors of the academic performance of Teaching English as a Second Language trainees. System, 32 (1), 75–87. DOI logoGoogle Scholar
Mostafa, T., Crossley, S., & Kim, Y. (2021). Predictors of English as second language learners’ oral proficiency development in a classroom context. International Journal of Applied Linguistics, 31 (3), 526–548. DOI logoGoogle Scholar
Nagy, W. E., & Scott, J. A. (2000). Vocabulary processes. In M. L. Kamil, P. Mosenthal, P. D. Pearson, & R. Barr. (Eds.), Handbook of reading research (Vol. 31, pp. 269–284). Mahwah, NJ: Earlbaum.Google Scholar
Nation, P., & Beglar, D. (2007). A vocabulary size test. The Language Teacher, 31 (7), 9–13.Google Scholar
Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36 (3), 402–407. DOI logoGoogle Scholar
Ortega, L. (2016). Multi-competence in second language acquisition: inroads into the mainstream? In V. Cook & L. Wei. (Eds) The Cambridge Handbook of Linguistic Multi-competence. Cambridge University Press. DOI logoGoogle Scholar
Paetzold, G., & Specia, L. (2016). Collecting and Exploring Everyday Language for Predicting Psycholinguistic Properties of Words. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 1669–1679. [URL]
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [URL]
Read, J. (1998). Validating a Test to Measure Depth of Vocabulary Knowledge. In Validation in Language Assessment. Routledge.Google Scholar
Řehůřek, R., & Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45–50.Google Scholar
Saito, K. (2020). Multi- or Single-Word Units? The Role of Collocation Use in Comprehensible and Contextually Appropriate Second Language Speech. Language Learning, 70 (2), 548–588. DOI logoGoogle Scholar
Sun, K., & Lu, X. (2021). Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach. Frontiers in Psychology, 12 1, 672243. DOI logoGoogle Scholar
Sundqvist, P. (2019). Commercial-off-the-shelf games in the digital wild and L2 learner vocabulary. Language Learning, 23 (1), 27.Google Scholar
Vanderbilt, Katia, “Developing and Testing Alternative Benchmarks of Lexical Sophistication: L2 Lexical Frequency, Semantic Context, and Word Recognition Indices.” Dissertation, Georgia State University, 2020. DOI logo
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. ArXiv:1706.03762 [Cs]. [URL]
Webb, S. (2008). Receptive and productive vocabulary sizes of L2 learners. Studies in Second Language Acquisition, 30 (1), 79–95. DOI logoGoogle Scholar
(2009). The Effects of Receptive and Productive Learning of Word Pairs on Vocabulary Knowledge. RELC Journal, 40 (3), 360–376. DOI logoGoogle Scholar
Wilson, M. (1988). MRC psycholinguistic database: Machine-usable dictionary, version 2.00. Behavior Research Methods, Instruments, & Computers, 20 (1), 6–10. DOI logoGoogle Scholar
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., … Rush, A. (2020). Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38–45. DOI logoGoogle Scholar
Zaytseva, V., Miralpeix, I., & Pérez-Vidal, C. (2019). Because words matter: Investigating vocabulary development across contexts and modalities. Language Teaching Research, 136216881985297. Google Scholar
Zhang, H., Chen, M., & Li, X. (2021). Developmental Features of Lexical Richness in English Writings by Chinese Beginner Learners. Frontiers in Psychology. DOI logoGoogle Scholar
Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M. (2018). Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation. 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1–8. DOI logoGoogle Scholar
Cited by (4)

Cited by four other publications

Cong, Yan
2024. Second language learning of degree expressions: A computational approach. Natural Language Processing ► pp. 1 ff. DOI logo
Cong, Yan
2025. Demystifying large language models in second language development research. Computer Speech & Language 89 ► pp. 101700 ff. DOI logo
Lohmann, Julian F., Fynn Junge, Jens Möller, Johanna Fleckenstein, Ruth Trüb, Stefan Keller, Thorben Jansen & Andrea Horbach
2024. Neural Networks or Linguistic Features? - Comparing Different Machine-Learning Approaches for Automated Assessment of Text Quality Traits Among L1- and L2-Learners’ Argumentative Essays. International Journal of Artificial Intelligence in Education DOI logo
Huang, Jingxiu, Xiaomin Wu, Jing Wen, Chenhan Huang, Mingrui Luo, Lixiang Liu & Yunxiang Zheng
2023. Evaluating Familiarity Ratings of Domain Concepts with Interpretable Machine Learning: A Comparative Study. Applied Sciences 13:23 ► pp. 12818 ff. DOI logo

This list is based on CrossRef data as of 10 january 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.